Successfully reported this slideshow.
Your SlideShare is downloading. ×

Iot analytics in wearables

More Related Content

Slideshows for you

Similar to Iot analytics in wearables

Iot analytics in wearables

  1. 1. Copyright : Futuretext Ltd. London0 Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges Hadi Banaee *, Mobyen Uddin Ahmed and Amy Loutfi Center for Applied Autonomous Sensor Systems, O¨ rebro University, SE- 70182 O¨ rebro, Sweden; E-Mails: (M.U.A.); (A.L.) Shift in focus from data collection and simple apps (calculating steps, sleep etc) to Data analytics based on context awareness and Personalization Specifically we concentrated the review on the following vital sign parameters: electrocardiogram (ECG), oxygen saturation (SpO2 ), heart rate (HR), Photoplethysmography (PPG), blood glucose (BG), respiratory rate (RR), and blood pressure (BP).
  2. 2. Copyright : Futuretext Ltd. London1 Three types of data mining tasks: Anomaly detection(including raising alarms) Prediction and Diagnosis Three analysis dimensions. a) Setting in which the monitoring occurs(ex independent living) b) Type of subjects used (ex healthy, specific illness etc) c) How and where data is processed
  3. 3. Copyright : Futuretext Ltd. London2 Anomaly Detection Anomaly detection techniques are often developed based on a classification methods to distinguish the data set into normal class and outliers. For example, support vector machines , Markov models and Wavelet analysis are used in health monitoring systems for anomaly detection. a) Usually deal with short term and multivariate data sets in order to characterize the entire the data to find discords. b) Finding irregular patterns in vital signs time series such as abnormal episodes in ECG pulses , SpO2 signal and blood glucose level which mostly discover unusual temporal patterns in continuous data. c) Use domain knowledge and predefined information to detect anomalies for decision making such as anomaly detection in sleep episodes and finding hazardous stress levels
  4. 4. Copyright : Futuretext Ltd. London3 Prediction Supervised learning models where it includes feature extraction, training and testing steps while performing the prediction of the behavior. Examples: blood glucose level prediction, mortality prediction by clustering electronic health data, and a predictive decision making system for dialysis patients. Diagnosis/Decision Making Like anomaly detection but not necessary detection abnormalities. Examples: estimating the severity of health episodes of patients suffering chronic disease, sleep issues such as polysomnography and apnea, estimation and classification of health conditions and emotion recognition. Most of these researches have used online databases with annotated episodes in order to have sufficient and trustable real-world disease labels to evaluate the decision making process. Considering the complexity of the data Neural Networks and decision trees used.
  5. 5. Copyright : Futuretext Ltd. London4 Other Data Mining Tasks for Wearable Sensors (1) data acquisition using the adequate sensor set; (2)transmission of data from subject to clinician; (3)integration of data with other descriptive data; and (4)data storage. Several data mining techniques are applied such as wavelet analysis for artifact reduction and data compression , rule-based methods for data summarizing and transmitting , and Gaussian process for secure authentication . Preprocessing (1) filter unusual data to remove artifacts and (2) remove high frequency noise Ex ECG data to remove frequency noise, the other methods in frequency domain
  6. 6. Copyright : Futuretext Ltd. London5 Time Domain Spectral Domain Other Features Mean R-R, Std R-R, Mean HR, Spectral energy [27,62], Power Std HR [39], Number of R-R spectral density [32], Low-pass ECG interval [27], Mean R-R, Std filter [45], Low/high - R-R interval [64]. frequency [39,64]. Mean, zero crossing counts, Drift from normality range [61], SpO2 entropy [48], Mean, Slope [61], Energy, Low frequency [60]. Self-similarity [60]. Entropy [60]. Energy, Low/high frequency [60], Low/high frequency [36], Mean, Slope [61], Mean, Wavelet coefficients of data Drift from normality range [61], HR Entropy, Co-occurrence Self-similarity, Std [60]. segments [45], Low/high frequency, Power spectral coefficients [60]. density [42]. Rise Times, Max, Min, PPG Mean [36]. Low/high frequency [36]. - BP Mean, Slope [61]. - Rule based features [56]. RR Mean, Min, Max [64]. - Residual and tidal volume [64]. Zero crossings count, Peak value, Rise time (EMG) [68], Spectral energy (EEG) [27], Mean, Duration (GSR) [36], Median and mean Frequency, Bandwidth, Peaks count Other Pick value, Min, Max Spectral energy (EMG) [68], (GSR) [36]. (SCR) [51], Total magnitude, Energy (GSR) [36]. Duration (GSR) [39].
  7. 7. Copyright : Futuretext Ltd. London6 Three most popular approaches for dimension reduction in medical domain are PCA, ICA, and LDA Other tools for feature selection used in the literature includes threshold- based rules, analysis of variance (ANOVA) , and Fourier transforms. Common health parameters considered by SVM methods are ECG, HR, and SpO2 which are mostly used in the short term and annotated form. In general, SVM techniques are often proposed for anomaly detection and decision making tasks in healthcare services. The ability of the NN is to model highly nonlinear systems such as physiological records where the correlation of the input parameters is not easily detectable
  8. 8. Copyright : Futuretext Ltd. London7
  9. 9. Copyright : Futuretext Ltd. London8 Data Properties • Time Horizon (long term/short term): Some data analysis systems in healthcare were designed to process short signals such as few minutes of ECG data , a few hours of heart rate or oxygen saturation and the measurement of blood pressures for a day and even more (Blood glucose). • Scale (large/small): considering a big number of subjects (patient or healthy) are counted as large scale studies [30]. • Labeling (annotated/unlabeled):annotations also acquired using another source of knowledge like electronic health record (EHR), coronary syndromes, and also history of vital signs • Continuous/Discrete: • Single Sensor/Multi Sensors:
  10. 10. Copyright : Futuretext Ltd. London9 Challenges • Need for Large scale monitoring in non-clinical context • Dealing with annotated data sets: few benchmark data sets are available also the challenge of how data annotation (labeling) can be best done for such target groups. • Multiple measurements: Another challenge in this field is to exploit the multiple measurements of vital signs simultaneously. Esp with sensor fusion techniques • Contextual information: • Reliability, level of trust to the system: the amount of trust between the data analysis system and the experts who use the system for decision making tasks. • Discovering of unseen features • Post processing